Deep Learning in Medical Ultrasound Analysis: A Review

By Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei, Li Liu, Shawn Li, Dong Ni, Tianfu Wang. Engineering, 2019

723 citations recorded by the source corpus.

Abstract Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0b706160de638668f72587b9df21f6361f57add4

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020, background): “However, very few works using DL on LUS images can be found in the literature [30].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “In [226] the authors introduced MetaQNN, a RL based meta-modeling algorithm to automatically generate CNN architectures for image classiﬁcation by using Q-learning [227] Asurvey on the state-of-the-art machine learning models in the context of NLP [263] Inﬂectional Review of Deep Learning on Natural”
Transformers in Medical Imaging: A Survey (2022, background): “Since their renaissance, CNNs have demonstrated remarkable improvements on numerous medical imaging modalities, including Radiography [5], Endoscopy [6], Computed Tomography (CT) [7], [8], Mammography Images (MG) [9], Ultrasound Images [10], Magnetic Resonance Imaging (MRI) [11], [12], and Positron ”